Advertisement

Manufacturing Operations, Internet of Things, and Big Data: Towards Predictive Manufacturing Systems

  • Radu F. BabiceanuEmail author
  • Remzi Seker
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 594)

Abstract

The recent leap advances in sensor and communication technologies made possible the Internet connectivity of the physical world: the Internet of Things, where not only documents and images are created, shared, or modified in the cyberspace, but also the physical resources interact over Internet and make decisions based on shared communication. The Big Data revolution has set the stage for the use of large data sets to predict the behaviour of consumers, organizations, and markets, taking into account the real-time outcomes of complex or unexpected events. Manufacturing can benefit from both these advances and move the manufacturing community closer towards the predictive manufacturing systems paradigm. Prediction in manufacturing operations could vary from simple resource failure prediction to more complex predictions of consumer behaviour and adaptation of manufacturing operations to address the expected changes in the business environment.

Keywords

Sensor-based real-time monitoring Big Data Internet of Things Predictive manufacturing systems 

References

  1. 1.
    Mejjaouli, S., Babiceanu, R.F.: Integrated monitoring and control system for production, supply chain, and logistics operations. In: Proceedings of the 24th International Conference on Flexible Automation and Intelligent Manufacturing, San Antonio, TX (2014)Google Scholar
  2. 2.
    Sun, C.: Application of RFID technology for logistics and internet of things. AASRI Procedia 1, 106–111 (2012)Google Scholar
  3. 3.
    Prabhu, N.: Design and Construction of an RFID-Enabled Infrastructure: The Next Avatar of the Internet. Taylor & Francis Group, Boca Raton (2014)Google Scholar
  4. 4.
    Cutler, T.R.: The internet of manufacturing things. Ind. Eng. 46(8), 37–41 (2014)Google Scholar
  5. 5.
    Chatziantoniou, D., Pramatari, K., Sotiropoulos, Y.: Supporting real-time supply chain decisions based on RFID data streams. J. Syst. Softw. 84, 700–710 (2011)CrossRefGoogle Scholar
  6. 6.
    Mejjaouli, S., Babiceanu, R.F.: Holonic condition monitoring and fault-recovery system for sustainable manufacturing enterprises. In: Borangiu, T., Thomas, A., Trentesaux, D. (eds.) Service Orientation in Holonic and Multi-agent Manufacturing and Robotics, pp. 31–46. Springer, Berlin (2014)Google Scholar
  7. 7.
    Manovich, L.: Trending: The promises and the challenges of big social data. In: Gold, M.K. (ed.) Debates in the Digital Humanities. The University of Minnesota Press, USA (2012). http://www.manovich.net/DOCS/Manovich_trending_paper.pdf
  8. 8.
    Mayer-Schonberger, V., Cukier, K.: Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Miffin Harcourt Publishing Company, New York (2013)Google Scholar
  9. 9.
    Berman, J.J.: Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information. Elsevier, Waltham (2013)Google Scholar
  10. 10.
    Lee, J., Lapira, E., Bagheri, B., Kao, H.-A.: Recent advances and trends in predictive manufacturing systems in big data environment. Manuf. Lett. 1, 38–41 (2013)CrossRefGoogle Scholar
  11. 11.
    Lee, J., Lapira, E., Yang, S., Kao, H.-A.: Predictive manufacturing system trends of next generation production systems. In: Proceedings of the 11th IFAC Workshop on Intelligent Manufacturing Systems, vol. 11(1), pp. 150–156 (2013)Google Scholar
  12. 12.
    Smith, D.: R is Hot: How Did a Statistical Programming Language Invented in New Zealand Become a Global Sensation. Revolution Analytics (2010). http://info.revolutionanalytics.com/R-is-Hot-Whitepaper.html
  13. 13.
    Prajapati, V.: Big Data Analytics with R and Hadoop: Set Up an Integrated Infrastructure of R and Hadoop to Turn Your Data Analytics into Big Data Analytics. Packt Publishing, Birmingham (2013)Google Scholar
  14. 14.
    Adler, J.: R in a Nutshell: A Desktop Quick Reference, 2nd edn. O’Reilly Media Inc, Sebastopol (2012)Google Scholar
  15. 15.
    Loshin, D.: Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph. Morgan Kaufmann, Waltham (2013)Google Scholar
  16. 16.
    Das, S., Sismanis, Y., Beyer, K.S., Gemulla R., Haas, P.J., McPherson, J.: Ricardo: integration R and Hadoop. In: Proceedings of the ACM/SIGMOD PODS Conference, Indianapolis, IN (2010)Google Scholar
  17. 17.
    Rutman, N.: Map/Reduce on Lustre: Hadoop Performance in HPC Environments. Xyratex (2011). http://www.xyratex.com/sites/default/files/Xyratex_white_paper_MapReduce_1-4.pdf

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Electrical, Computer, Software, and Systems EngineeringEmbry-Riddle Aeronautical UniversityDaytona BeachUSA

Personalised recommendations